{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import feather\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some notes about the data \n",
    "\n",
    "* TAXINCL - Mortgage payment includes property taxes (0-N/A, 1-No, 2-Yes)\n",
    "* INSINCL - Mortgage payment includes property insurance (0-N/A, 1-No, 2-Yes)\n",
    "------------------\n",
    "* HHINCOME - Total household income\n",
    "* INCTOT - Total personal income; 9999999 is for N/A\n",
    "* INCEARN - -$9999 is for 0 income or loss\n",
    "------------------\n",
    "* SERIAL is an 8-digit numeric variable which assigns a unique identification number to each household record in a given sample (See PERNUM for the analogous person record identifier). A combination of YEAR, DATANUM, and SERIAL provides a unique identifier for every household in the IPUMS; the combination of YEAR, DATANUM, SERIAL, and PERNUM uniquely identifies every person in the database. \n",
    "* DATANUM identifies the particular sample from which the case is drawn in a given year. For most censuses, the IPUMS has multiple datasets available which were constructed using different sampling techniques (i.e. size/demographic of the sample population, geographic coverage level or location, or duration of the sampling period for the ACS/PRCS samples).\n",
    "* CLUSTER - Household cluster for variance estimation, > 30K factors\n",
    "------------------\n",
    "* Columns with only NA values\n",
    "               'MOBLHOM2',\n",
    "               'VET95X00',\n",
    "               'VET90X95',\n",
    "               'MOVEDINORIG',\n",
    "               'LUNCHSUB',\n",
    "               'RENTSUB',\n",
    "               'MET2003',\n",
    "               'MOBLOAN',\n",
    "               'SECRESRE',\n",
    "               'HEATSUB',\n",
    "               'BUILTYR',\n",
    "               'PUBHOUS',\n",
    "               'SECRESMO',\n",
    "               'SECRES'\n",
    "----------------------\n",
    "* Columns with a single factor level\n",
    "                'CNTRY' - UN code for the country (only US)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the full data from feather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # reading in the full data\n",
    "# start = time.time()\n",
    "# df_full = feather.read_dataframe('/home/hkaren/data/census/ipums_2000-2015.feather')\n",
    "# print time.time()-start\n",
    "# df_full.shape\n",
    "# # takes ~1100 sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # basic filtering before sampling\n",
    "# df_full = df_full[df_full.YEAR > 2005]\n",
    "# df_full = df_full[df_full.INCEARN > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # randomly sampling n rows\n",
    "# start = time.time()\n",
    "# df_small = df_full.sample(n = 1000000, random_state=1234)\n",
    "# print time.time()-start\n",
    "# df_small.shape\n",
    "# del df_full\n",
    "# feather.write_dataframe(df_small, '/home/hkaren/data/census/ipums_2000-2015_small.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the sampled data from feather "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = '/home/hkaren/data/census/'\n",
    "df = feather.read_dataframe(path + 'ipums_2000-2015_small.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RECTYPE' 'YEAR' 'DATANUM' 'SERIAL' 'NUMPREC' 'SUBSAMP' 'HHWT' 'HHTYPE'\n",
      " 'REPWT' 'CLUSTER' 'ADJUST' 'CPI99' 'REGION' 'STATEICP' 'STATEFIP' 'COUNTY'\n",
      " 'COUNTYFIPS' 'METRO' 'METAREA' 'METAREAD' 'MET2013' 'MET2013ERR' 'CITY'\n",
      " 'CITYERR' 'CITYPOP' 'PUMA' 'PUMARES2MIG' 'STRATA' 'PUMASUPR' 'CONSPUMA'\n",
      " 'CPUMA0010' 'APPAL' 'APPALD' 'HOMELAND' 'MET2003' 'CNTRY' 'GQ' 'GQTYPE'\n",
      " 'GQTYPED' 'FARM' 'OWNERSHP' 'OWNERSHPD' 'MORTGAGE' 'MORTGAG2' 'COMMUSE'\n",
      " 'FARMPROD' 'ACREHOUS' 'MORTAMT1' 'MORTAMT2' 'TAXINCL' 'INSINCL' 'PROPINSR'\n",
      " 'PROPTX99' 'OWNCOST' 'RENT' 'RENTGRS' 'RENTMEAL' 'CONDOFEE' 'MOBLHOME'\n",
      " 'MOBLHOM2' 'MOBLOAN' 'SECRES' 'SECRESMO' 'SECRESRE' 'COSTELEC' 'COSTGAS'\n",
      " 'COSTWATR' 'COSTFUEL' 'HHINCOME' 'PUBHOUS' 'RENTSUB' 'HEATSUB' 'LUNCHSUB'\n",
      " 'FOODSTMP' 'FDSTPAMT' 'VALUEH' 'LINGISOL' 'VACANCY' 'KITCHEN'\n",
      " 'KITCHENORIG' 'FRIDGE' 'FRIDGEORIG' 'SINK' 'STOVE' 'ROOMS' 'ROOMSORIG'\n",
      " 'PLUMBING' 'HOTWATER' 'SHOWER' 'TOILET' 'BUILTYR' 'BUILTYR2' 'UNITSSTR'\n",
      " 'BEDROOMS' 'BEDROOMSORIG' 'PHONE' 'PHONEORIG' 'CILAPTOP' 'CIHAND'\n",
      " 'CIOTHCOMP' 'CINETHH' 'CIMODEM' 'CISAT' 'CIDSL' 'CIFIBER' 'CIBRDBND'\n",
      " 'CIDIAL' 'CIOTHSVC' 'FUELHEAT' 'VEHICLES' 'SSMC' 'NFAMS' 'NSUBFAM'\n",
      " 'NCOUPLES' 'NMOTHERS' 'NFATHERS' 'MULTGEN' 'MULTGEND' 'CBNSUBFAM' 'REPWT1'\n",
      " 'REPWT2' 'REPWT3' 'REPWT4' 'REPWT5' 'REPWT6' 'REPWT7' 'REPWT8' 'REPWT9'\n",
      " 'REPWT10' 'REPWT11' 'REPWT12' 'REPWT13' 'REPWT14' 'REPWT15' 'REPWT16'\n",
      " 'REPWT17' 'REPWT18' 'REPWT19' 'REPWT20' 'REPWT21' 'REPWT22' 'REPWT23'\n",
      " 'REPWT24' 'REPWT25' 'REPWT26' 'REPWT27' 'REPWT28' 'REPWT29' 'REPWT30'\n",
      " 'REPWT31' 'REPWT32' 'REPWT33' 'REPWT34' 'REPWT35' 'REPWT36' 'REPWT37'\n",
      " 'REPWT38' 'REPWT39' 'REPWT40' 'REPWT41' 'REPWT42' 'REPWT43' 'REPWT44'\n",
      " 'REPWT45' 'REPWT46' 'REPWT47' 'REPWT48' 'REPWT49' 'REPWT50' 'REPWT51'\n",
      " 'REPWT52' 'REPWT53' 'REPWT54' 'REPWT55' 'REPWT56' 'REPWT57' 'REPWT58'\n",
      " 'REPWT59' 'REPWT60' 'REPWT61' 'REPWT62' 'REPWT63' 'REPWT64' 'REPWT65'\n",
      " 'REPWT66' 'REPWT67' 'REPWT68' 'REPWT69' 'REPWT70' 'REPWT71' 'REPWT72'\n",
      " 'REPWT73' 'REPWT74' 'REPWT75' 'REPWT76' 'REPWT77' 'REPWT78' 'REPWT79'\n",
      " 'REPWT80' 'RESPMODE' 'PERNUM' 'PERWT' 'SLWT' 'REPWTP' 'FAMSIZE' 'NCHILD'\n",
      " 'NCHLT5' 'FAMUNIT' 'ELDCH' 'YNGCH' 'NSIBS' 'MOMLOC' 'STEPMOM' 'MOMRULE'\n",
      " 'POPLOC' 'STEPPOP' 'POPRULE' 'SPLOC' 'SPRULE' 'SUBFAM' 'SFTYPE' 'SFRELATE'\n",
      " 'CBSUBFAM' 'CBSFTYPE' 'CBSFRELATE' 'RELATE' 'RELATED' 'SEX' 'AGE'\n",
      " 'AGEORIG' 'BIRTHQTR' 'MARST' 'BIRTHYR' 'MARRNO' 'MARRINYR' 'YRMARR'\n",
      " 'DIVINYR' 'WIDINYR' 'FERTYR' 'RACE' 'RACED' 'HISPAN' 'HISPAND' 'BPL'\n",
      " 'BPLD' 'ANCESTR1' 'ANCESTR1D' 'ANCESTR2' 'ANCESTR2D' 'CITIZEN' 'YRNATUR'\n",
      " 'YRIMMIG' 'YRSUSA1' 'YRSUSA2' 'LANGUAGE' 'LANGUAGED' 'SPEAKENG' 'TRIBE'\n",
      " 'TRIBED' 'RACESING' 'RACESINGD' 'RACAMIND' 'RACASIAN' 'RACBLK' 'RACPACIS'\n",
      " 'RACWHT' 'RACOTHER' 'RACNUM' 'SCHOOL' 'EDUC' 'EDUCD' 'GRADEATT'\n",
      " 'GRADEATTD' 'SCHLTYPE' 'DEGFIELD' 'DEGFIELDD' 'DEGFIELD2' 'DEGFIELD2D'\n",
      " 'EMPSTAT' 'EMPSTATD' 'LABFORCE' 'OCC' 'OCC1950' 'OCC1990' 'OCC2010' 'IND'\n",
      " 'IND1950' 'IND1990' 'CLASSWKR' 'CLASSWKRD' 'OCCSOC' 'INDNAICS' 'WKSWORK1'\n",
      " 'WKSWORK2' 'UHRSWORK' 'WRKLSTWK' 'ABSENT' 'LOOKING' 'AVAILBLE' 'WRKRECAL'\n",
      " 'WORKEDYR' 'INCTOT' 'FTOTINC' 'INCWAGE' 'INCBUS00' 'INCSS' 'INCWELFR'\n",
      " 'INCINVST' 'INCRETIR' 'INCSUPP' 'INCOTHER' 'INCEARN' 'POVERTY' 'OCCSCORE'\n",
      " 'SEI' 'HWSEI' 'PRESGL' 'PRENT' 'ERSCOR50' 'ERSCOR90' 'EDSCOR50' 'EDSCOR90'\n",
      " 'NPBOSS50' 'NPBOSS90' 'MIGRATE1' 'MIGRATE1D' 'MIGPLAC1' 'MIGMET1'\n",
      " 'MIGTYPE1' 'MIGCITY1' 'MIGPUMS1' 'MIGPUMA1' 'MOVEDIN' 'MOVEDINORIG'\n",
      " 'DISABWRK' 'VETDISAB' 'DIFFREM' 'DIFFPHYS' 'DIFFMOB' 'DIFFCARE' 'DIFFSENS'\n",
      " 'DIFFEYE' 'DIFFHEAR' 'VETSTAT' 'VETSTATD' 'VET01LTR' 'VET95X00' 'VET90X01'\n",
      " 'VET90X95' 'VET75X90' 'VET80X90' 'VET75X80' 'VETVIETN' 'VET55X64'\n",
      " 'VETKOREA' 'VET47X50' 'VETWWII' 'VETOTHER' 'VETYRS' 'PWSTATE2' 'PWMETRO'\n",
      " 'PWCITY' 'PWTYPE' 'PWPUMA00' 'PWPUMAS' 'TRANWORK' 'CARPOOL' 'RIDERS'\n",
      " 'TRANTIME' 'DEPARTS' 'ARRIVES' 'GCHOUSE' 'GCMONTHS' 'GCRESPON' 'PROBAI'\n",
      " 'PROBAPI' 'PROBBLK' 'PROBOTH' 'PROBWHT' 'REPWTP1' 'REPWTP2' 'REPWTP3'\n",
      " 'REPWTP4' 'REPWTP5' 'REPWTP6' 'REPWTP7' 'REPWTP8' 'REPWTP9' 'REPWTP10'\n",
      " 'REPWTP11' 'REPWTP12' 'REPWTP13' 'REPWTP14' 'REPWTP15' 'REPWTP16'\n",
      " 'REPWTP17' 'REPWTP18' 'REPWTP19' 'REPWTP20' 'REPWTP21' 'REPWTP22'\n",
      " 'REPWTP23' 'REPWTP24' 'REPWTP25' 'REPWTP26' 'REPWTP27' 'REPWTP28'\n",
      " 'REPWTP29' 'REPWTP30' 'REPWTP31' 'REPWTP32' 'REPWTP33' 'REPWTP34'\n",
      " 'REPWTP35' 'REPWTP36' 'REPWTP37' 'REPWTP38' 'REPWTP39' 'REPWTP40'\n",
      " 'REPWTP41' 'REPWTP42' 'REPWTP43' 'REPWTP44' 'REPWTP45' 'REPWTP46'\n",
      " 'REPWTP47' 'REPWTP48' 'REPWTP49' 'REPWTP50' 'REPWTP51' 'REPWTP52'\n",
      " 'REPWTP53' 'REPWTP54' 'REPWTP55' 'REPWTP56' 'REPWTP57' 'REPWTP58'\n",
      " 'REPWTP59' 'REPWTP60' 'REPWTP61' 'REPWTP62' 'REPWTP63' 'REPWTP64'\n",
      " 'REPWTP65' 'REPWTP66' 'REPWTP67' 'REPWTP68' 'REPWTP69' 'REPWTP70'\n",
      " 'REPWTP71' 'REPWTP72' 'REPWTP73' 'REPWTP74' 'REPWTP75' 'REPWTP76'\n",
      " 'REPWTP77' 'REPWTP78' 'REPWTP79' 'REPWTP80']\n"
     ]
    }
   ],
   "source": [
    "c = df.columns.values\n",
    "col_names = list(c)\n",
    "print c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income related columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "income_cols = [col for col in col_names if 'INC' in col]\n",
    "# remove non income cols\n",
    "income_cols.remove('TAXINCL') \n",
    "income_cols.remove('INSINCL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HHINCOME',\n",
       " 'INCTOT',\n",
       " 'FTOTINC',\n",
       " 'INCWAGE',\n",
       " 'INCBUS00',\n",
       " 'INCSS',\n",
       " 'INCWELFR',\n",
       " 'INCINVST',\n",
       " 'INCRETIR',\n",
       " 'INCSUPP',\n",
       " 'INCOTHER',\n",
       " 'INCEARN']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HHINCOME 33945\n",
      "INCTOT 16545\n",
      "FTOTINC 32924\n",
      "INCWAGE 1015\n",
      "INCBUS00 1256\n",
      "INCSS 520\n",
      "INCWELFR 286\n",
      "INCINVST 1032\n",
      "INCRETIR 690\n",
      "INCSUPP 244\n",
      "INCOTHER 552\n",
      "INCEARN 4411\n"
     ]
    }
   ],
   "source": [
    "for cols in income_cols:\n",
    "    print cols, df[cols].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d5c2dc090>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ef40e5f50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 570
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.INCEARN > 100000].INCEARN.hist(bins=200, figsize = (9,6), normed = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inc_corr = df[income_cols].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col4 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col5 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col6 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col7 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col8 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col9 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col10 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col11 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col0 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col1 {\n",
       "            \n",
       "                background-color:  #57ac60;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col2 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col3 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col4 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col5 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col6 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col7 {\n",
       "            \n",
       "                background-color:  #def0e0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col8 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col9 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col10 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col11 {\n",
       "            \n",
       "                background-color:  #51a85a;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col0 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col1 {\n",
       "            \n",
       "                background-color:  #b5dbb9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col2 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col3 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col4 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col5 {\n",
       "            \n",
       "                background-color:  #e9f5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col6 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col7 {\n",
       "            \n",
       "                background-color:  #e6f4e7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col8 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col9 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col10 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row4_col11 {\n",
       "            \n",
       "                background-color:  #b5dbb9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col0 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col1 {\n",
       "            \n",
       "                background-color:  #e0f0e1;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col2 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col3 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col4 {\n",
       "            \n",
       "                background-color:  #eaf6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col5 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col6 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col7 {\n",
       "            \n",
       "                background-color:  #e7f4e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col8 {\n",
       "            \n",
       "                background-color:  #d1e9d4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col9 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col10 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col11 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col0 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col1 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col2 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col3 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col4 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col5 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col6 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col7 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col8 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col9 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col10 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row6_col11 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col0 {\n",
       "            \n",
       "                background-color:  #edf7ee;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col1 {\n",
       "            \n",
       "                background-color:  #acd6b0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col2 {\n",
       "            \n",
       "                background-color:  #edf7ee;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col3 {\n",
       "            \n",
       "                background-color:  #d5ebd7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col4 {\n",
       "            \n",
       "                background-color:  #e0f0e1;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col5 {\n",
       "            \n",
       "                background-color:  #e0f0e1;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col6 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col7 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col8 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col9 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col10 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row7_col11 {\n",
       "            \n",
       "                background-color:  #d1e9d4;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col0 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col1 {\n",
       "            \n",
       "                background-color:  #d5ebd7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col2 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col3 {\n",
       "            \n",
       "                background-color:  #edf7ee;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col4 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col5 {\n",
       "            \n",
       "                background-color:  #cde7d0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col6 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col7 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col8 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col9 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col10 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row8_col11 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col0 {\n",
       "            \n",
       "                background-color:  #e9f5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col1 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col2 {\n",
       "            \n",
       "                background-color:  #e9f5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col3 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col4 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col5 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col6 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col7 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col8 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col9 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col10 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col11 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col0 {\n",
       "            \n",
       "                background-color:  #edf7ee;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col1 {\n",
       "            \n",
       "                background-color:  #e6f4e7;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col2 {\n",
       "            \n",
       "                background-color:  #edf7ee;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col3 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col4 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col5 {\n",
       "            \n",
       "                background-color:  #ebf6ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col6 {\n",
       "            \n",
       "                background-color:  #fce8ed;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col7 {\n",
       "            \n",
       "                background-color:  #fbe4e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col8 {\n",
       "            \n",
       "                background-color:  #f0f5f0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col9 {\n",
       "            \n",
       "                background-color:  #fce7ec;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col10 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col11 {\n",
       "            \n",
       "                background-color:  #f7edf0;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col0 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col1 {\n",
       "            \n",
       "                background-color:  #4ca655;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col2 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col3 {\n",
       "            \n",
       "                background-color:  #51a85a;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col4 {\n",
       "            \n",
       "                background-color:  #b9ddbd;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col5 {\n",
       "            \n",
       "                background-color:  #fbe5ea;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col6 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col7 {\n",
       "            \n",
       "                background-color:  #daeedc;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col8 {\n",
       "            \n",
       "                background-color:  #fce6eb;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col9 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col10 {\n",
       "            \n",
       "                background-color:  #fbe3e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col11 {\n",
       "            \n",
       "                background-color:  #46a350;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th class=\"blank level0\" >\n",
       "                  \n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col0\" colspan=1>\n",
       "                  HHINCOME\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col1\" colspan=1>\n",
       "                  INCTOT\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col2\" colspan=1>\n",
       "                  FTOTINC\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col3\" colspan=1>\n",
       "                  INCWAGE\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col4\" colspan=1>\n",
       "                  INCBUS00\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col5\" colspan=1>\n",
       "                  INCSS\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col6\" colspan=1>\n",
       "                  INCWELFR\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col7\" colspan=1>\n",
       "                  INCINVST\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col8\" colspan=1>\n",
       "                  INCRETIR\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col9\" colspan=1>\n",
       "                  INCSUPP\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col10\" colspan=1>\n",
       "                  INCOTHER\n",
       "                \n",
       "                \n",
       "                \n",
       "                <th class=\"col_heading level0 col11\" colspan=1>\n",
       "                  INCEARN\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row0\" rowspan=1>\n",
       "                    HHINCOME\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col0\"\n",
       "                 class=\"data row0 col0\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col1\"\n",
       "                 class=\"data row0 col1\" >\n",
       "                    -0.0680904\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col2\"\n",
       "                 class=\"data row0 col2\" >\n",
       "                    0.9999\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col3\"\n",
       "                 class=\"data row0 col3\" >\n",
       "                    -0.0694756\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col4\"\n",
       "                 class=\"data row0 col4\" >\n",
       "                    -0.00881622\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col5\"\n",
       "                 class=\"data row0 col5\" >\n",
       "                    -0.0261494\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col6\"\n",
       "                 class=\"data row0 col6\" >\n",
       "                    0.00249729\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col7\"\n",
       "                 class=\"data row0 col7\" >\n",
       "                    -0.00192555\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col8\"\n",
       "                 class=\"data row0 col8\" >\n",
       "                    -0.0189522\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col9\"\n",
       "                 class=\"data row0 col9\" >\n",
       "                    0.0185475\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col10\"\n",
       "                 class=\"data row0 col10\" >\n",
       "                    -0.000955908\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row0_col11\"\n",
       "                 class=\"data row0 col11\" >\n",
       "                    -0.0689478\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row1\" rowspan=1>\n",
       "                    INCTOT\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col0\"\n",
       "                 class=\"data row1 col0\" >\n",
       "                    -0.0680904\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col1\"\n",
       "                 class=\"data row1 col1\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col2\"\n",
       "                 class=\"data row1 col2\" >\n",
       "                    -0.0679007\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col3\"\n",
       "                 class=\"data row1 col3\" >\n",
       "                    0.890723\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col4\"\n",
       "                 class=\"data row1 col4\" >\n",
       "                    0.328246\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col5\"\n",
       "                 class=\"data row1 col5\" >\n",
       "                    0.0759992\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col6\"\n",
       "                 class=\"data row1 col6\" >\n",
       "                    -0.0167912\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col7\"\n",
       "                 class=\"data row1 col7\" >\n",
       "                    0.382312\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col8\"\n",
       "                 class=\"data row1 col8\" >\n",
       "                    0.142211\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col9\"\n",
       "                 class=\"data row1 col9\" >\n",
       "                    -0.0125545\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col10\"\n",
       "                 class=\"data row1 col10\" >\n",
       "                    0.0337486\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row1_col11\"\n",
       "                 class=\"data row1 col11\" >\n",
       "                    0.962535\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row2\" rowspan=1>\n",
       "                    FTOTINC\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col0\"\n",
       "                 class=\"data row2 col0\" >\n",
       "                    0.9999\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col1\"\n",
       "                 class=\"data row2 col1\" >\n",
       "                    -0.0679007\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col2\"\n",
       "                 class=\"data row2 col2\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col3\"\n",
       "                 class=\"data row2 col3\" >\n",
       "                    -0.0693762\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col4\"\n",
       "                 class=\"data row2 col4\" >\n",
       "                    -0.008738\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col5\"\n",
       "                 class=\"data row2 col5\" >\n",
       "                    -0.0257179\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col6\"\n",
       "                 class=\"data row2 col6\" >\n",
       "                    0.00247729\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col7\"\n",
       "                 class=\"data row2 col7\" >\n",
       "                    -0.00184359\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col8\"\n",
       "                 class=\"data row2 col8\" >\n",
       "                    -0.0186835\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col9\"\n",
       "                 class=\"data row2 col9\" >\n",
       "                    0.0185763\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col10\"\n",
       "                 class=\"data row2 col10\" >\n",
       "                    -0.000940269\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row2_col11\"\n",
       "                 class=\"data row2 col11\" >\n",
       "                    -0.0688251\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row3\" rowspan=1>\n",
       "                    INCWAGE\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col0\"\n",
       "                 class=\"data row3 col0\" >\n",
       "                    -0.0694756\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col1\"\n",
       "                 class=\"data row3 col1\" >\n",
       "                    0.890723\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col2\"\n",
       "                 class=\"data row3 col2\" >\n",
       "                    -0.0693762\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col3\"\n",
       "                 class=\"data row3 col3\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col4\"\n",
       "                 class=\"data row3 col4\" >\n",
       "                    -0.0410497\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col5\"\n",
       "                 class=\"data row3 col5\" >\n",
       "                    -0.0490898\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col6\"\n",
       "                 class=\"data row3 col6\" >\n",
       "                    -0.0254576\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row3_col7\"\n",
       "                 class=\"data row3 col7\" >\n",
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       "                \n",
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       "                \n",
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       "                \n",
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       "                \n",
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       "            \n",
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       "                \n",
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       "                \n",
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       "                \n",
       "                \n",
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       "                \n",
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       "                 class=\"data row4 col8\" >\n",
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       "                \n",
       "                \n",
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       "            \n",
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       "                \n",
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       "                \n",
       "                \n",
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       "                \n",
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       "                \n",
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       "                \n",
       "                \n",
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       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row5_col5\"\n",
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       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
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       "                \n",
       "                \n",
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       "                \n",
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       "                \n",
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       "                \n",
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       "                \n",
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       "                 class=\"data row6 col6\" >\n",
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       "                \n",
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       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col9\"\n",
       "                 class=\"data row9 col9\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col10\"\n",
       "                 class=\"data row9 col10\" >\n",
       "                    0.00616257\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row9_col11\"\n",
       "                 class=\"data row9 col11\" >\n",
       "                    -0.027702\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row10\" rowspan=1>\n",
       "                    INCOTHER\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col0\"\n",
       "                 class=\"data row10 col0\" >\n",
       "                    -0.000955908\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col1\"\n",
       "                 class=\"data row10 col1\" >\n",
       "                    0.0337486\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col2\"\n",
       "                 class=\"data row10 col2\" >\n",
       "                    -0.000940269\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col3\"\n",
       "                 class=\"data row10 col3\" >\n",
       "                    -0.0204868\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col4\"\n",
       "                 class=\"data row10 col4\" >\n",
       "                    -0.00620051\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col5\"\n",
       "                 class=\"data row10 col5\" >\n",
       "                    0.0253399\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col6\"\n",
       "                 class=\"data row10 col6\" >\n",
       "                    0.0151565\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col7\"\n",
       "                 class=\"data row10 col7\" >\n",
       "                    0.00748246\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col8\"\n",
       "                 class=\"data row10 col8\" >\n",
       "                    0.0352119\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col9\"\n",
       "                 class=\"data row10 col9\" >\n",
       "                    0.00616257\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col10\"\n",
       "                 class=\"data row10 col10\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row10_col11\"\n",
       "                 class=\"data row10 col11\" >\n",
       "                    -0.0216458\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                \n",
       "                <th id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769\"\n",
       "                 class=\"row_heading level0 row11\" rowspan=1>\n",
       "                    INCEARN\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col0\"\n",
       "                 class=\"data row11 col0\" >\n",
       "                    -0.0689478\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col1\"\n",
       "                 class=\"data row11 col1\" >\n",
       "                    0.962535\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col2\"\n",
       "                 class=\"data row11 col2\" >\n",
       "                    -0.0688251\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col3\"\n",
       "                 class=\"data row11 col3\" >\n",
       "                    0.931085\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col4\"\n",
       "                 class=\"data row11 col4\" >\n",
       "                    0.326274\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col5\"\n",
       "                 class=\"data row11 col5\" >\n",
       "                    -0.0326808\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col6\"\n",
       "                 class=\"data row11 col6\" >\n",
       "                    -0.0254939\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col7\"\n",
       "                 class=\"data row11 col7\" >\n",
       "                    0.16463\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col8\"\n",
       "                 class=\"data row11 col8\" >\n",
       "                    0.0051029\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col9\"\n",
       "                 class=\"data row11 col9\" >\n",
       "                    -0.027702\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col10\"\n",
       "                 class=\"data row11 col10\" >\n",
       "                    -0.0216458\n",
       "                \n",
       "                \n",
       "                \n",
       "                <td id=\"T_df7c7a58_1cb7_11e7_828e_0cc47ad95769row11_col11\"\n",
       "                 class=\"data row11 col11\" >\n",
       "                    1\n",
       "                \n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.formats.style.Styler at 0x7f2ebd6fed10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=sns.diverging_palette(1, 130, l=60, sep = 1, as_cmap=True)\n",
    "inc_corr.style.background_gradient(cmap=cm, low = 0.9, high = 0)\n",
    "#ax = sns.heatmap(inc_corr, cmap=cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Removing income columns\n",
    "for col in income_cols:\n",
    "    if col != \"INCEARN\":\n",
    "        del df[col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('O'), dtype('int32'), dtype('float64')], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what datatypes are in the data\n",
    "dt = df.dtypes\n",
    "dt.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    273\n",
       "int32      172\n",
       "object       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_dtype_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RECTYPE</th>\n",
       "      <th>OCCSOC</th>\n",
       "      <th>INDNAICS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H</td>\n",
       "      <td>1110XX</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H</td>\n",
       "      <td>435071</td>\n",
       "      <td>332MZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H</td>\n",
       "      <td>435021</td>\n",
       "      <td>532M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H</td>\n",
       "      <td>512090</td>\n",
       "      <td>3272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>433031</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RECTYPE  OCCSOC INDNAICS\n",
       "0       H  1110XX      712\n",
       "1       H  435071    332MZ\n",
       "2       H  435021     532M\n",
       "3       H  512090     3272\n",
       "4       H  433031      531"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['O']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['H'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RECTYPE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding categorical columns\n",
    "for col in df.select_dtypes(include=['O']).columns:\n",
    "    df[col+'_le'] = LabelEncoder().fit_transform(list(df[col].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# selecting only numerical columns\n",
    "init_num_cols = [e for e in df.columns if e not in df.select_dtypes(include=['O']).columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ID columns and features with large number of factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dropping ID columns\n",
    "id_cols = ['SERIAL', 'PERNUM']\n",
    "for col in id_cols:\n",
    "    del df[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9.940606e-312\n",
       "1    9.940625e-312\n",
       "2    9.920876e-312\n",
       "3    9.920863e-312\n",
       "4    9.940603e-312\n",
       "Name: CLUSTER, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CLUSTER column is unsinged int64, but casted as float64 by feather\n",
    "df['CLUSTER'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dropping CLUSTER column\n",
    "# categorical with > 30k factors\n",
    "del df['CLUSTER']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def corr_df(x, corr_val):\n",
    "    '''\n",
    "    Obj: Drops features that are strongly correlated to other features.\n",
    "          This lowers model complexity, and aids in generalizing the model.\n",
    "    Inputs:\n",
    "          df: features df (x)\n",
    "          corr_val: Columns are dropped relative to the corr_val input (e.g. 0.8)\n",
    "    Output: df that only includes uncorrelated features\n",
    "    '''\n",
    "\n",
    "    # Creates Correlation Matrix and Instantiates\n",
    "    corr_matrix = x.corr()\n",
    "    #corr_matrix = x\n",
    "    iters = range(len(corr_matrix.columns))\n",
    "    drop_cols = []\n",
    "    drop_col_names = []\n",
    "\n",
    "    # Iterates through Correlation Matrix Table to find correlated columns\n",
    "    for i in iters:\n",
    "        for j in range(i):\n",
    "            item = corr_matrix.iloc[j:j+1, i:i+1]\n",
    "            col = item.columns\n",
    "            row = item.index\n",
    "            val = item.values\n",
    "            if val >= corr_val:\n",
    "                # Prints the correlated feature set and the corr val\n",
    "                print(col.values[0], \"|\", row.values[0], \"|\", round(val[0][0], 3))\n",
    "                drop_cols.append(i)\n",
    "                drop_col_names.append(col.values[0])\n",
    "\n",
    "    # drops = sorted(set(drop_cols))[::-1]\n",
    "    drops = list(set(drop_col_names))\n",
    "    \n",
    "    return drops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('COUNTYFIPS', '|', 'COUNTY', '|', 1.0)\n",
      "('METAREAD', '|', 'METAREA', '|', 1.0)\n",
      "('PUMARES2MIG', '|', 'PUMA', '|', 0.998)\n",
      "('STRATA', '|', 'PUMA', '|', 1.0)\n",
      "('STRATA', '|', 'PUMARES2MIG', '|', 0.998)\n",
      "('PUMASUPR', '|', 'STATEFIP', '|', 1.0)\n",
      "('CPUMA0010', '|', 'STATEFIP', '|', 0.996)\n",
      "('CPUMA0010', '|', 'PUMASUPR', '|', 0.996)\n",
      "('APPALD', '|', 'APPAL', '|', 1.0)\n",
      "('GQTYPED', '|', 'GQTYPE', '|', 1.0)\n",
      "('OWNERSHPD', '|', 'OWNERSHP', '|', 0.99)\n",
      "('RENTGRS', '|', 'RENT', '|', 0.99)\n",
      "('VALUEH', '|', 'OWNCOST', '|', 0.999)\n",
      "('KITCHENORIG', '|', 'KITCHEN', '|', 0.995)\n",
      "('FRIDGEORIG', '|', 'FRIDGE', '|', 0.996)\n",
      "('SINK', '|', 'FRIDGE', '|', 0.992)\n",
      "('SINK', '|', 'FRIDGEORIG', '|', 0.992)\n",
      "('ROOMSORIG', '|', 'ROOMS', '|', 0.995)\n",
      "('PLUMBING', '|', 'SINK', '|', 0.991)\n",
      "('HOTWATER', '|', 'FRIDGE', '|', 0.992)\n",
      "('HOTWATER', '|', 'FRIDGEORIG', '|', 0.991)\n",
      "('HOTWATER', '|', 'SINK', '|', 0.995)\n",
      "('HOTWATER', '|', 'PLUMBING', '|', 0.994)\n",
      "('SHOWER', '|', 'SINK', '|', 0.991)\n",
      "('SHOWER', '|', 'PLUMBING', '|', 0.993)\n",
      "('TOILET', '|', 'PLUMBING', '|', 0.993)\n",
      "('TOILET', '|', 'SHOWER', '|', 0.992)\n",
      "('BEDROOMSORIG', '|', 'BEDROOMS', '|', 0.994)\n",
      "('MULTGEND', '|', 'MULTGEN', '|', 0.997)\n",
      "('SLWT', '|', 'PERWT', '|', 1.0)\n",
      "('YNGCH', '|', 'ELDCH', '|', 0.998)\n",
      "('RELATED', '|', 'RELATE', '|', 1.0)\n",
      "('AGEORIG', '|', 'AGE', '|', 0.999)\n",
      "('WIDINYR', '|', 'YRMARR', '|', 0.993)\n",
      "('RACED', '|', 'RACE', '|', 0.999)\n",
      "('HISPAND', '|', 'HISPAN', '|', 0.997)\n",
      "('BPLD', '|', 'BPL', '|', 1.0)\n",
      "('ANCESTR1D', '|', 'ANCESTR1', '|', 1.0)\n",
      "('ANCESTR2D', '|', 'ANCESTR2', '|', 1.0)\n",
      "('LANGUAGED', '|', 'LANGUAGE', '|', 1.0)\n",
      "('TRIBED', '|', 'TRIBE', '|', 1.0)\n",
      "('RACESINGD', '|', 'RACESING', '|', 0.996)\n",
      "('EDUCD', '|', 'EDUC', '|', 0.998)\n",
      "('GRADEATT', '|', 'SCHOOL', '|', 0.994)\n",
      "('GRADEATTD', '|', 'SCHOOL', '|', 0.996)\n",
      "('GRADEATTD', '|', 'GRADEATT', '|', 1.0)\n",
      "('DEGFIELDD', '|', 'DEGFIELD', '|', 1.0)\n",
      "('DEGFIELD2D', '|', 'DEGFIELD2', '|', 1.0)\n",
      "('EMPSTATD', '|', 'EMPSTAT', '|', 0.997)\n",
      "('OCC2010', '|', 'OCC', '|', 0.994)\n",
      "('EDSCOR90', '|', 'EDSCOR50', '|', 0.996)\n",
      "('NPBOSS50', '|', 'ERSCOR50', '|', 0.993)\n",
      "('NPBOSS50', '|', 'EDSCOR50', '|', 0.99)\n",
      "('NPBOSS90', '|', 'ERSCOR90', '|', 0.993)\n",
      "('NPBOSS90', '|', 'EDSCOR90', '|', 0.991)\n",
      "('NPBOSS90', '|', 'NPBOSS50', '|', 0.992)\n",
      "('MIGRATE1D', '|', 'MIGRATE1', '|', 0.992)\n",
      "('VETSTATD', '|', 'VETSTAT', '|', 0.994)\n",
      "('VETWWII', '|', 'VET47X50', '|', 0.993)\n",
      "('VETOTHER', '|', 'VET47X50', '|', 0.998)\n",
      "('VETOTHER', '|', 'VETWWII', '|', 0.994)\n",
      "('ARRIVES', '|', 'DEPARTS', '|', 0.99)\n",
      "('PROBBLK', '|', 'RACBLK', '|', 0.996)\n",
      "('OCCSOC_le', '|', 'OCC', '|', 0.999)\n",
      "('OCCSOC_le', '|', 'OCC2010', '|', 0.993)\n",
      "('INDNAICS_le', '|', 'IND', '|', 0.998)\n"
     ]
    }
   ],
   "source": [
    "corr_cols = corr_df(df, 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['METAREA', 'METAREAD', 'PUMASUPR', 'CONSPUMA', 'APPAL', 'APPALD', 'MET2003', 'MOBLHOM2', 'MOBLOAN', 'SECRES', 'SECRESMO', 'SECRESRE', 'PUBHOUS', 'RENTSUB', 'HEATSUB', 'LUNCHSUB', 'FDSTPAMT', 'KITCHENORIG', 'FRIDGE', 'FRIDGEORIG', 'SINK', 'STOVE', 'ROOMSORIG', 'HOTWATER', 'SHOWER', 'TOILET', 'BUILTYR', 'BEDROOMSORIG', 'PHONEORIG', 'CILAPTOP', 'CIHAND', 'CIOTHCOMP', 'CINETHH', 'CIMODEM', 'CISAT', 'CIDSL', 'CIFIBER', 'CIBRDBND', 'CIDIAL', 'CIOTHSVC', 'SSMC', 'AGEORIG', 'MARRNO', 'MARRINYR', 'YRMARR', 'DIVINYR', 'WIDINYR', 'YRNATUR', 'RACESING', 'RACESINGD', 'DEGFIELD', 'DEGFIELDD', 'DEGFIELD2', 'DEGFIELD2D', 'WKSWORK1', 'MIGMET1', 'MIGTYPE1', 'MIGCITY1', 'MIGPUMS1', 'MOVEDINORIG', 'DISABWRK', 'VETDISAB', 'DIFFEYE', 'DIFFHEAR', 'VET95X00', 'VET90X95', 'VET80X90', 'VET75X80', 'VETYRS', 'PWMETRO', 'PWCITY', 'PWTYPE', 'PWPUMAS', 'PROBAI', 'PROBAPI', 'PROBBLK', 'PROBOTH', 'PROBWHT']\n"
     ]
    }
   ],
   "source": [
    "# Find the columns with NAs\n",
    "na_cols = df.columns[df.isnull().any()].tolist()\n",
    "print na_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering columns based on number of unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hkaren/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col_name</th>\n",
       "      <th>N_unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNCOST</td>\n",
       "      <td>8627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INCEARN</td>\n",
       "      <td>4411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RENTGRS</td>\n",
       "      <td>3534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STRATA</td>\n",
       "      <td>3344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VALUEH</td>\n",
       "      <td>1896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>REPWTP72</td>\n",
       "      <td>1275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>REPWTP48</td>\n",
       "      <td>1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>REPWTP10</td>\n",
       "      <td>1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>REPWTP62</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>REPWTP41</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>REPWTP40</td>\n",
       "      <td>1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>REPWTP28</td>\n",
       "      <td>1264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>REPWTP74</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>REPWTP68</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>REPWTP73</td>\n",
       "      <td>1261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>REPWTP27</td>\n",
       "      <td>1259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>REPWTP69</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>REPWTP44</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>REPWTP24</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>REPWTP80</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>REPWTP60</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>REPWTP30</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>REPWTP12</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>REPWTP36</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>REPWTP9</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>REPWTP56</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>REPWTP54</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>REPWTP50</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>REPWTP70</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>REPWTP59</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>DIFFCARE</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>LABFORCE</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>DIFFMOB</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>RACOTHER</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>HOMELAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>RACPACIS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>FOODSTMP</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>DIFFSENS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>CNTRY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>DATANUM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>REPWTP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>RECTYPE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>WORKEDYR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>RECTYPE_le</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>REPWT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>VACANCY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>MOBLHOM2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>VET95X00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>VET90X95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>MOVEDINORIG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>LUNCHSUB</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>RENTSUB</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>MET2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>MOBLOAN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>SECRESRE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>HEATSUB</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>BUILTYR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>PUBHOUS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>SECRESMO</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>SECRES</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Col_name  N_unique\n",
       "0        OWNCOST      8627\n",
       "1        INCEARN      4411\n",
       "2        RENTGRS      3534\n",
       "3         STRATA      3344\n",
       "4         VALUEH      1896\n",
       "5       REPWTP72      1275\n",
       "6       REPWTP48      1273\n",
       "7       REPWTP10      1273\n",
       "8       REPWTP62      1269\n",
       "9       REPWTP41      1266\n",
       "10      REPWTP40      1265\n",
       "11      REPWTP28      1264\n",
       "12      REPWTP74      1262\n",
       "13      REPWTP68      1262\n",
       "14      REPWTP73      1261\n",
       "15      REPWTP27      1259\n",
       "16      REPWTP69      1258\n",
       "17      REPWTP44      1257\n",
       "18      REPWTP24      1257\n",
       "19      REPWTP80      1257\n",
       "20      REPWTP60      1256\n",
       "21      REPWTP30      1256\n",
       "22      REPWTP12      1255\n",
       "23      REPWTP36      1255\n",
       "24       REPWTP9      1255\n",
       "25      REPWTP56      1254\n",
       "26      REPWTP54      1254\n",
       "27      REPWTP50      1254\n",
       "28      REPWTP70      1253\n",
       "29      REPWTP59      1253\n",
       "..           ...       ...\n",
       "418     DIFFCARE         2\n",
       "419     LABFORCE         2\n",
       "420      DIFFMOB         2\n",
       "421     RACOTHER         2\n",
       "422     HOMELAND         2\n",
       "423     RACPACIS         2\n",
       "424     FOODSTMP         2\n",
       "425     DIFFSENS         2\n",
       "426        CNTRY         1\n",
       "427      DATANUM         1\n",
       "428       REPWTP         1\n",
       "429      RECTYPE         1\n",
       "430     WORKEDYR         1\n",
       "431   RECTYPE_le         1\n",
       "432        REPWT         1\n",
       "433      VACANCY         1\n",
       "434     MOBLHOM2         0\n",
       "435     VET95X00         0\n",
       "436     VET90X95         0\n",
       "437  MOVEDINORIG         0\n",
       "438     LUNCHSUB         0\n",
       "439      RENTSUB         0\n",
       "440      MET2003         0\n",
       "441      MOBLOAN         0\n",
       "442     SECRESRE         0\n",
       "443      HEATSUB         0\n",
       "444      BUILTYR         0\n",
       "445      PUBHOUS         0\n",
       "446     SECRESMO         0\n",
       "447       SECRES         0\n",
       "\n",
       "[448 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nunique_dic = {}\n",
    "for col in df.columns.values:\n",
    "     nunique_dic[col] = df[col].nunique()\n",
    "nunique_df = pd.DataFrame(nunique_dic.items(), columns = ['Col_name', 'N_unique'])\n",
    "nunique_df.sort(['N_unique'], inplace = True, ascending=False)\n",
    "nunique_df.reset_index(drop=True, inplace = True)\n",
    "nunique_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SECRES'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_na_cols = ['MOBLHOM2',\n",
    "               'VET95X00',\n",
    "               'VET90X95',\n",
    "               'MOVEDINORIG',\n",
    "               'LUNCHSUB',\n",
    "               'RENTSUB',\n",
    "               'MET2003',\n",
    "               'MOBLOAN',\n",
    "               'SECRESRE',\n",
    "               'HEATSUB',\n",
    "               'BUILTYR',\n",
    "               'PUBHOUS',\n",
    "               'SECRESMO',\n",
    "               'SECRES']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n",
      "1000000\n"
     ]
    }
   ],
   "source": [
    "# all these columns contain only NAs\n",
    "# sum should be equal to nrows if all rows are True\n",
    "for col in all_na_cols:\n",
    "    print df[col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping all NA columns and columns with 1 unique value (zero variance)\n",
    "for col in all_na_cols:\n",
    "    del df[col]\n",
    "\n",
    "for col in nunique_df[nunique_df['N_unique'] == 1]['Col_name'].values:\n",
    "    del df[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hkaren/anaconda2/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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IkSOSJH9/f0lSmzZtdPLkSZWWllbKOq59uDQryenYjmzbtm0lSa1bt5Yk69Qs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\nAAAYo1gAAAAAMEaxAAAAAGC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Hrq6uVWYPHTpkZaWLRzMu17JlS6dZZ9txLVkvLy95e3tb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\nNszTJcFNtm7dqt27d2vmzJny9fX1dDkoAbm5uZL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94a7b350>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
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     },
     "output_type": "display_data"
    },
    {
     "data": {
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vC2fOnNF3332nLl261Jh57733NG/ePONLuc25c+eUmpoqNzc3Pfjgg5KkkJAQ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\nCxYsUEpKioYOHarJkyersLBQ7777riI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2efcee8bd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WllJSUjRgwADNnz/fMduvXz/1799f06dPV1pamiTp8OHDSkhIUFBQkJYvXy6r1SpJGjRo\nkAYNGqTp06crNDRUbm5uKikpUXx8vBo1aqTk5GR5eXlJkgYPHqyoqCjNnTtX4eHhqlevniRp2rRp\nqlOnjpKTk1W/fn3H7JgxY5SUlKSIiAgFBARUdxcCAAAAf0jVPrNgPyNw8eVGktSqVSvVq1dPeXl5\nki78RV+SYmNjneYCAwMVHBysLVu26NSpU1ecbdCggfr06aM9e/Zo//79kqR169aprKxM0dHRjlCQ\nJB8fHw0ePFi//vqr0tPTJUmbN2/WyZMnFRUV5QgFSbJarRo+fLjOnj2rjRs3SpJ27dqlnJwcPfTQ\nQ45QsIuJiZHNZtNHH31U1d0FAAAA3DSqHQutWrWSJOXm5jotP336tE6dOqXWrVtLknbv3i1/f381\nbNiwwjo6deqksrIyZWdnO2atVqvx3oROnTpJuvBm3j4rScHBwVed/eabbyRJQUFBFWbtr1WVWfsM\nAAAAUBtV+zKk0aNH68svv3Rc/3/33Xfr2LFjWrhwoSwWi8aPH6/CwkIVFBRUuInZzt/fX5J06NAh\nSVJeXp78/Pzk5uZ22dmDBw86ZqULZx0u1ahRI+OsKViqMuvj4yNfX1/HbHVs37692uvA1bnqfnbV\n39vVcJxdA8fZNXCca7+b7RhXOxbatGmjVatW6W9/+5uio6Mdy+vXr6/33ntPXbt21dGjRyVJHh4e\nxnXYLwkqKipy/LRHQWVmrVar3N3dK8x6enpWmL14eWVmL7fNnp6ejhkAAACgNqp2LPz0008aNWqU\nSktL9eKLL+ruu+/Wb7/9pmXLlmn06NFauHCh7rnnnuuxrbVSSEjIDX/Nm61or4ea2M81yX6MXe33\ndjUcZ9fAcXYNHOfaryaPcXXe+1U7Fl5++WUdPXpUGzZsUNOmTR3LBwwYoL59++rFF1/Uhg0bJEkl\nJSXGddj/Qu/t7e34ebnZ4uJiSRcuBbLPnj9/XqWlpRXOLlw6a/9pX16Z2Stth30GAAAAqI2qdYNz\ncXGxduzYocDAQKdQkC5cvmO/BOnIkSPy8/NTfn6+cT2HDx+WJLVo0UKS1LRpUx0/flylpaUVZu33\nElw8K8m4bvts8+bNJUlNmjSRJMdlUaZtuHTWtN7Tp0/r9OnTjlkAAACgNqpWLJw5c0Y2m01nz541\nPm5/s3/27FkFBwcrPz/f8ab8YpmZmfLw8FC7du0kXfhko/LycscnE13Mfhqlc+fOjlnpwjctX27W\nfrrH/hzTqZjMzEzjrGm9l84CAAAAtVG1YsHPz08tWrTQvn379MMPPzg9VlBQoG3btsnHx0dt2rRR\nZGSkJFX4luSMjAxlZ2dr4MCBjsuQIiIiZLFYKszm5uZq06ZN6tatm5o1ayZJCgsLk4eHh5KSknTu\n3DnH7IkTJ5SWlqZmzZqpW7dukqRevXrprrvuUkpKigoLCx2zpaWlSk5Olq+vrwYMGCBJuvfeexUY\nGKiNGzc6nV2w2WxKTEyUm5ubhgwZUo29BwAAAPyxWadOnTq1Oito3LixNmzYoHXr1qmkpERHjx7V\ntm3b9NJLL+nYsWN66aWX1KlTJ7Vs2VLff/+9UlNTdeTIERUVFWnz5s2aNWuW7rjjDs2bN88RC3fe\neacKCwuVmpqqvXv3qqysTNu2bdMrr7wim82mBQsWOL5l2cvLSz4+PkpJSVFmZqZsNpt27typadOm\n6dixY5o3b54jLKxWq5o1a6YPPvhAn3/+uSwWi7777jvFx8dr7969mj59utN3OwQEBCg1NVUbN26U\nxWJRTk6O5s6dq/T0dI0bN059+vS55v125MgRSf/3ka030pEjR7Rl96kb/ro16dH+rvVN2zX57xdu\nHI6za+A4uwaOc+1X0+/9rvW1LTabzVbdDdi5c6eWLl2qHTt26NSpU/L29lb79u01cuRI9erVyzFX\nWlqqd955R2vXrlVeXp58fX3Vs2dPPfPMMxU+KtVmsyk5OVmrV69Wbm6uPD091bVrV02YMMH46Urr\n1q1TYmKi9u/fL6vVqqCgIMXFxTkuJ7pYenq6Fi9erOzsbNlsNgUEBGjUqFEKDQ2tMLt7924tWLBA\nWVlZKi0tVatWrRQTE6OIiIhq7bOaviN+6j8P3fDXrUlr3/zvmt6EG4pP1XANHGfXwHF2DRzn2q+m\n3/td62tfl1hA1dX0vzDEQu3Gf3RcA8fZNXCcXQPHufar6fd+1/ra1bpnAQAAAEDtRSwAAAAAMCIW\nAAAAABgRCwAAAACMiAUAAAAARsQCAAAAACNiAQAAAIARsQAAAADAiFgAAAAAYEQsAAAAADAiFgAA\nAAAYEQsAAAAAjIgFAAAAAEbEAgAAAAAjYgEAAACAEbEAAAAAwIhYAAAAAGBELAAAAAAwIhYAAAAA\nGBELAAAAAIyIBQAAAABGxAIAAAAAI2IBAAAAgBGxAAAAAMCIWAAAAABgRCwAAAAAMCIWAAAAABgR\nCwAAAACMiAUAAAAARsQCAAAAACNiAQAAAIARsQAAAADAiFgAAAAAYEQsAAAAADAiFgAAAAAYEQsA\nAAAAjIgFAAAAAEbEAgAAAAAjYgEAAACAEbEAAAAAwIhYAAAAAGBELAAAAAAwIhYAAAAAGBELAAAA\nAIyIBQAAAABGxAIAAAAAI2IBAAAAgBGxAAAAAMCIWAAAAABgRCwAAAAAMCIWAAAAABgRCwAAAACM\niAUAAAAARsQCAAAAACNiAQAAAIARsQAAAADAiFgAAAAAYEQsAAAAADAiFgAAAAAYEQsAAAAAjIgF\nAAAAAEbEAgAAAAAjYgEAAACAEbEAAAAAwIhYAAAAAGBELAAAAAAwIhYAAAAAGBELAAAAAIyIBQAA\nAABGxAIAAAAAI2IBAAAAgBGxAAAAAMCIWAAAAABgRCwAAAAAMCIWAAAAABgRCwAAAACMiAUAAAAA\nRsQCAAAAAKPrEguff/65YmJiFBwcrC5duig2NlZbt26tMHfmzBnNnz9f/fv3V/v27dW9e3dNmDBB\nOTk5FWbLy8u1fPlyhYeHq0OHDrrvvvs0atQoffPNN8ZtSEtLU0REhIKCghQcHKwRI0boyy+/NM5u\n2bJF0dHRCg4OVqdOnRQZGal169YZZ3fs2KEnnnhCXbp0UYcOHRQeHq6kpCTZbLYq7CEAAADg5lPt\nWEhJSdGoUaMkSS+99JLi4uJ06NAhPfnkk/rqq68cczabTWPGjNHixYsVEhKi+Ph4PfHEE8rIyNCw\nYcP0888/O6138uTJmj17tlq0aKEZM2Zo/PjxysnJUUxMjLKyspxmExISNGnSJHl7e+vll1/WpEmT\nVFRUpCeffFKffPKJ0+yaNWs0evRoFRcXa+LEiZoyZYq8vLz03HPPKTEx0Wl269atio2N1YEDBxQX\nF6cZM2aoZcuWmjlzpuLj46u76wAAAIA/tFur8+Rjx45p1qxZ+q//+i+99957uuWWC+0RGhqqRx55\nRFu2bFG3bt0kSevXr1d6eroef/xxTZw40bGOHj16KCIiQnPmzNGiRYskSVlZWUpJSdGAAQM0f/58\nx2y/fv3Uv39/TZ8+XWlpaZKkw4cPKyEhQUFBQVq+fLmsVqskadCgQRo0aJCmT5+u0NBQubm5qaSk\nRPHx8WrUqJGSk5Pl5eUlSRo8eLCioqI0d+5chYeHq169epKkadOmqU6dOkpOTlb9+vUds2PGjFFS\nUpIiIiIUEBBQnV0IAAAA/GFV68xCWlqaiouLFRcX5wgFSWratKn+85//6O9//7tj2Zo1ayRJsbGx\nTusIDAxUcHCwtmzZolOnTl1xtkGDBurTp4/27Nmj/fv3S5LWrVunsrIyRUdHO0JBknx8fDR48GD9\n+uuvSk9PlyRt3rxZJ0+eVFRUlCMUJMlqtWr48OE6e/asNm7cKEnatWuXcnJy9NBDDzlCwS4mJkY2\nm00fffTRNew1AAAA4OZQrTML//nPf+Tt7a3g4GBJ0vnz53X+/Hm5u7tXmN29e7f8/f3VsGHDCo91\n6tRJO3bsUHZ2tnr06KHdu3fLarWqY8eOxtm1a9dq165dat26tXbv3i1Jjm24dFa68Mb/z3/+s+N+\nh6CgoAqz9tfatWuXoqOjKzV7ufsnqmL79u3VXgeuzlX3s6v+3q6G4+waOM6ugeNc+91sx7haZxZ+\n+uknNWvWTN99951iYmLUoUMHdejQQWFhYVq/fr1jrrCwUAUFBcZQkCR/f39J0qFDhyRJeXl58vPz\nk5ub22VnDx486JiVLpx1uFSjRo2Ms6btqMqsj4+PfH19HbMAAABAbVStMwsnT57UrbfeqqeeekpD\nhw7V448/rry8PL3zzjt69tlnVVxcrKioKBUVFUmSPDw8jOuxXxJknysqKnJEQWVmrVar8WyGp6dn\nhdmLl1dm9nLb7Onp6ZipjpCQkGqvo6putqK9HmpiP9ck+zF2td/b1XCcXQPH2TVwnGu/mjzG1Xnv\nV61YKCsrU15ent544w2Fh4c7lvfu3VsDBw7UvHnzNHTo0Oq8BAAAAIAaUq3LkLy8vFSnTh0NGjTI\naXnTpk3VrVs3HT9+XD/++KN8fHwkSSUlJcb12P9C7+3t7fh5udni4mJJcqzT29tb58+fV2lp6VVn\n7T/tyysze6XtsM8AAAAAtVG1YqFx48YqLy83Pmb/+NHCwkJ5e3vLz89P+fn5xtnDhw9Lklq0aCHp\nQmwcP37cGAD2ewkunpVkXLd9tnnz5pKkJk2aSJKOHj162W24dNa03tOnT+v06dOOWQAAAKA2qlYs\nBAUFqaysTD/88EOFx+xvvu03CAcHBys/P9+x/GKZmZny8PBQu3btHLPl5eXatWtXhVn7NVedO3d2\nzEoXvmn5crP2a8P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ERkYGSkpKADDj0aC+vh4FBQVYtGgR0tPT0d/fD5PJhN7e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e92a44290>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MGKC4uDjt3LmzUe7MmTNasWKFRo4cqT59+mjw4MGaNWuWiouLG2Xr6uqUnJys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\nfy8vL/vPprLV1dUNtunl5aX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ebdbd6bd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OjlZoaKjuuecezZo1Sx999JHTY8jOzlZsbKzCw8MVERGhxx57TLt27XKazc/P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\nWN6W7OWOw5EBAAAAOiKXFYu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94925f10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/+cnyc+Ui1/+z3Gbq855pWOeZgXWeGWZSnWfaXMTK2WrM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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e948658d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MWRQUFECj0fyl5PQnlNT58OHDOHXqFHQ6HRISEtCnTx+U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\niIiIZLFZICIiIiIiWf8D6LQgU/rwJT4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9470bfd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pf/yjRo4caU+/77779NBDD2nJkiV6+OGHXd7Ilqxfv37XfZ03W6NtCs2xn5uT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\n1UZERKhv376aNWuWsrOzJUmFhYVKS0tTcHCw0tP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94769d50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lio2NVdu2bZWQkCB3d3dJUmhoqMaMGaOlS5cqJCREd999tyRp3rx5atq0qRIS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\n0aNH9d5776l169b6zW9+Y1/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W2JAp0YwzbKOEZIo1XMobNMbCAIrsQuf3B7OMK0dQzi6H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\nKDYLREREREQkis0CERERERGJ+n+fQHT2+okvrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e9469a0d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WeHi4XnvtNS1dulSSlJ2dreTkZA0ePFiLFy+21w4cOFCDBg1SXFycUlNTJUlH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\nVqtViYmJcnZ21vDhwx05fQA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94652b10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AADBGsQAAAABgjGIBAAAAwBjFAgAAAIAxigUAAAAAYxQL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\nyPS9OAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94593450>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAIyIBQAAAABGxAIAAAAAI2IBAAAAgBGxAAAAAMCIWAAAAABgRCwA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e944b3c10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wRVkAAAAAYIqyAAAAAMAUZQEAAACAKcoCAAAAAFOUBQAAAACmKAsAAAAATFEW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\nNmyYPYfPoWasOlar7hXcOG+Io1sAAACoduwKC3P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r3Cx7xnnDhg3661//qpYtWyo6Oloe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\nBQAAAACmCAsAAAAATP0/O7cu9qm6B0MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94402810>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vv/9+DRgwQPv27dOBAwckSRs3blRlZaViYmLMoiBJPj4+ioqK0okTJ5STkyNJ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\nAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQA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SfSlubm62/8adwZFx9vX1lZubmw4d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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e94341310>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HqnFgnBsHxrlxYJwbvvr+7ne7266zaywAAAAANFwUCwAAAADGKBYAAAAAjFEs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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10de3510>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10cf8b10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D6e/t6dOna+fOnUpNTVVSUpJCQ0PldruVl5en\n3r176/XXX2+054Lamcy4f//+euGFF7RlyxYlJCRo+PDhCgoK0sGDB5Wdna3g4GBNnjy5UZ8Pardw\n4UJJN37/bt26tdZ9qucsqVkeh/lZlmX5NAEa1dKlS5Wenl7nPtu3b1dYWJjCw8NrfENL0sGDB/X5\n559r3759qqioUM+ePZWYmMgbQJsQkznn5ORo9uzZdT524MCByszMtC0vGsaOn+dbVc+fS6CaDjvm\nXFxcrM8++0w7duxQaWmpHnjgAQ0bNkwpKSkKDAz0ZnzUg+mMq6qqtG7dOuXk5KiwsFCVlZUKCQnR\ns88+q5SUFC53ayLCw8Nvu0/1nKv3b27HYRQLAAAAAMa43SwAAAAAYxQLAAAAAMYoFgAAAACMUSwA\nAAAAGKNYAAAAADBGsQAAAABgjGIBAAAAwBjFAgAAAIAxigUAAAAAYxQLAAAAAMYoFgAAAACMUSwA\nAAAAGKNYAAAAADBGsQAAAABgjGIBAAAAwBjFAgAAAIAxigUAAAAAY/8PptgOEkkK6KsAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10c2bf50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RUU8++aSJowcAAAB8t1nmz58/v7521rFjR73//vtKTU1VWVmZTp8+rb179+rV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\nmbjD0qVLNW3aNPn4+Cg1NbVaZw0bwnEYZyxQbd7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10b73950>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w9wAAAICbU6PPLGRnZ+uJJ57Qjh07NHbsWM2fP19jx47V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\nAEwRFgAAAACY+n8JU62429/cGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10aa5dd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YwsPDuWAbwE+Cm8UjOwEAAAAY4hoLAAAAAMYoFgAAAACMUSwAAAAAGKNYAAAAADBGsQAA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\nYwQLAAAAAMb+D+yAxK7T0iMnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e109e4250>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hYfzc09OzWh0A4P+3d/8xUdd/HMCfZ9+uMKC8iFJIM+tz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\nNmHFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e107927d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Q0CWztvszAgMDa2Rt+7qcrC3z1VdfyWq1Osx26NBBzZs3r/XeEAAAAKAxqJenQpWUlOj4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\nAGMECwAAAADG/g8PPWDZCz91iwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e108df910>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 395
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in nunique_df[nunique_df['N_unique'] == 2]['Col_name'].values:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    df[col].hist()\n",
    "    plt.title(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.,   nan,  100.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PROBOTH.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 426)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns with small variance (near const)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_var = pd.DataFrame(df.std()>1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_var.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_name</th>\n",
       "      <th>nonzero_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YEAR</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NUMPREC</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUBSAMP</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HHWT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HHTYPE</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col_name nonzero_std\n",
       "0     YEAR        True\n",
       "1  NUMPREC        True\n",
       "2  SUBSAMP        True\n",
       "3     HHWT        True\n",
       "4   HHTYPE        True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_var.columns = ['col_name', 'nonzero_std']\n",
    "col_var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_name</th>\n",
       "      <th>nonzero_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [col_name, nonzero_std]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_var[col_var.nonzero_std == False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** after previous filtering, there are no more columns left with near zero variance. Will try once more after NA imputation **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catCols = df.select_dtypes(include=['O']).columns.values\n",
    "numCols = df.select_dtypes(include=['float64', 'int64', 'int32']).columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dft = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se = VarianceThreshold()\n",
    "# df_var = se.fit_transform(df[numCols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dft[numCols] = dft[numCols].fillna(dft[numCols].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('COUNTYFIPS', '|', 'COUNTY', '|', 1.0)\n",
      "('METAREAD', '|', 'METAREA', '|', 1.0)\n",
      "('PUMARES2MIG', '|', 'PUMA', '|', 0.998)\n",
      "('STRATA', '|', 'PUMA', '|', 1.0)\n",
      "('STRATA', '|', 'PUMARES2MIG', '|', 0.998)\n",
      "('CPUMA0010', '|', 'STATEFIP', '|', 0.996)\n",
      "('APPALD', '|', 'APPAL', '|', 1.0)\n",
      "('GQTYPED', '|', 'GQTYPE', '|', 1.0)\n",
      "('OWNERSHPD', '|', 'OWNERSHP', '|', 0.99)\n",
      "('RENTGRS', '|', 'RENT', '|', 0.99)\n",
      "('VALUEH', '|', 'OWNCOST', '|', 0.999)\n",
      "('SINK', '|', 'FRIDGE', '|', 0.992)\n",
      "('HOTWATER', '|', 'FRIDGE', '|', 0.992)\n",
      "('HOTWATER', '|', 'SINK', '|', 0.995)\n",
      "('SHOWER', '|', 'SINK', '|', 0.991)\n",
      "('TOILET', '|', 'SHOWER', '|', 0.992)\n",
      "('MULTGEND', '|', 'MULTGEN', '|', 0.997)\n",
      "('SLWT', '|', 'PERWT', '|', 1.0)\n",
      "('YNGCH', '|', 'ELDCH', '|', 0.998)\n",
      "('RELATED', '|', 'RELATE', '|', 1.0)\n",
      "('WIDINYR', '|', 'YRMARR', '|', 0.993)\n",
      "('RACED', '|', 'RACE', '|', 0.999)\n",
      "('HISPAND', '|', 'HISPAN', '|', 0.997)\n",
      "('BPLD', '|', 'BPL', '|', 1.0)\n",
      "('ANCESTR1D', '|', 'ANCESTR1', '|', 1.0)\n",
      "('ANCESTR2D', '|', 'ANCESTR2', '|', 1.0)\n",
      "('LANGUAGED', '|', 'LANGUAGE', '|', 1.0)\n",
      "('TRIBED', '|', 'TRIBE', '|', 1.0)\n",
      "('RACESINGD', '|', 'RACESING', '|', 0.996)\n",
      "('EDUCD', '|', 'EDUC', '|', 0.998)\n",
      "('GRADEATT', '|', 'SCHOOL', '|', 0.994)\n",
      "('GRADEATTD', '|', 'SCHOOL', '|', 0.996)\n",
      "('GRADEATTD', '|', 'GRADEATT', '|', 1.0)\n",
      "('DEGFIELDD', '|', 'DEGFIELD', '|', 1.0)\n",
      "('DEGFIELD2D', '|', 'DEGFIELD2', '|', 1.0)\n",
      "('EMPSTATD', '|', 'EMPSTAT', '|', 0.997)\n",
      "('OCC2010', '|', 'OCC', '|', 0.994)\n",
      "('EDSCOR90', '|', 'EDSCOR50', '|', 0.996)\n",
      "('NPBOSS50', '|', 'ERSCOR50', '|', 0.993)\n",
      "('NPBOSS50', '|', 'EDSCOR50', '|', 0.99)\n",
      "('NPBOSS90', '|', 'ERSCOR90', '|', 0.993)\n",
      "('NPBOSS90', '|', 'EDSCOR90', '|', 0.991)\n",
      "('NPBOSS90', '|', 'NPBOSS50', '|', 0.992)\n",
      "('MIGRATE1D', '|', 'MIGRATE1', '|', 0.992)\n",
      "('VETSTATD', '|', 'VETSTAT', '|', 0.994)\n",
      "('VETWWII', '|', 'VET47X50', '|', 0.993)\n",
      "('VETOTHER', '|', 'VET47X50', '|', 0.998)\n",
      "('VETOTHER', '|', 'VETWWII', '|', 0.994)\n",
      "('ARRIVES', '|', 'DEPARTS', '|', 0.99)\n",
      "('OCCSOC_le', '|', 'OCC', '|', 0.999)\n",
      "('OCCSOC_le', '|', 'OCC2010', '|', 0.993)\n",
      "('INDNAICS_le', '|', 'IND', '|', 0.998)\n"
     ]
    }
   ],
   "source": [
    "dft_corr_cols = corr_df(dft, 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "print len(dft_corr_cols)\n",
    "print len(corr_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Number of correlated columns above 0.99 threshold decreased after mean imputation **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_categorical(df, n_unique):\n",
    "    # n_unique: number of unique levels\n",
    "    categorical_cols = []\n",
    "    for col in df.columns.values:\n",
    "        if df[col].nunique() <= n_unique:\n",
    "            categorical_cols.append(col)\n",
    "    \n",
    "    return categorical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194\n",
      "['YEAR', 'NUMPREC', 'SUBSAMP', 'HHTYPE', 'ADJUST', 'CPI99', 'REGION', 'STATEICP', 'STATEFIP', 'METRO', 'MET2013ERR', 'CITYERR', 'APPAL', 'APPALD', 'HOMELAND', 'GQ', 'GQTYPE', 'GQTYPED', 'FARM', 'OWNERSHP', 'OWNERSHPD', 'MORTGAGE', 'MORTGAG2', 'COMMUSE', 'FARMPROD', 'ACREHOUS', 'TAXINCL', 'INSINCL', 'PROPTX99', 'RENTMEAL', 'COSTELEC', 'COSTGAS', 'FOODSTMP', 'LINGISOL', 'KITCHEN', 'KITCHENORIG', 'FRIDGE', 'FRIDGEORIG', 'SINK', 'STOVE', 'ROOMS', 'ROOMSORIG', 'PLUMBING', 'HOTWATER', 'SHOWER', 'TOILET', 'BUILTYR2', 'UNITSSTR', 'BEDROOMS', 'BEDROOMSORIG', 'PHONE', 'PHONEORIG', 'CILAPTOP', 'CIHAND', 'CIOTHCOMP', 'CINETHH', 'CIMODEM', 'CISAT', 'CIDSL', 'CIFIBER', 'CIBRDBND', 'CIDIAL', 'CIOTHSVC', 'FUELHEAT', 'VEHICLES', 'SSMC', 'NFAMS', 'NSUBFAM', 'NCOUPLES', 'NMOTHERS', 'NFATHERS', 'MULTGEN', 'MULTGEND', 'CBNSUBFAM', 'RESPMODE', 'FAMSIZE', 'NCHILD', 'NCHLT5', 'FAMUNIT', 'ELDCH', 'YNGCH', 'NSIBS', 'MOMLOC', 'STEPMOM', 'MOMRULE', 'POPLOC', 'STEPPOP', 'POPRULE', 'SPLOC', 'SPRULE', 'SUBFAM', 'SFTYPE', 'SFRELATE', 'CBSUBFAM', 'CBSFTYPE', 'CBSFRELATE', 'RELATE', 'RELATED', 'SEX', 'AGE', 'AGEORIG', 'BIRTHQTR', 'MARST', 'BIRTHYR', 'MARRNO', 'MARRINYR', 'YRMARR', 'DIVINYR', 'WIDINYR', 'FERTYR', 'RACE', 'HISPAN', 'HISPAND', 'CITIZEN', 'YRNATUR', 'YRIMMIG', 'YRSUSA1', 'YRSUSA2', 'LANGUAGE', 'SPEAKENG', 'TRIBE', 'TRIBED', 'RACESING', 'RACESINGD', 'RACAMIND', 'RACASIAN', 'RACBLK', 'RACPACIS', 'RACWHT', 'RACOTHER', 'RACNUM', 'SCHOOL', 'EDUC', 'EDUCD', 'GRADEATT', 'GRADEATTD', 'SCHLTYPE', 'DEGFIELD', 'DEGFIELD2', 'EMPSTAT', 'EMPSTATD', 'LABFORCE', 'CLASSWKR', 'CLASSWKRD', 'WKSWORK1', 'WKSWORK2', 'UHRSWORK', 'WRKLSTWK', 'ABSENT', 'LOOKING', 'AVAILBLE', 'WRKRECAL', 'OCCSCORE', 'SEI', 'MIGRATE1', 'MIGRATE1D', 'MIGTYPE1', 'MIGCITY1', 'MIGPUMS1', 'MOVEDIN', 'DISABWRK', 'VETDISAB', 'DIFFREM', 'DIFFPHYS', 'DIFFMOB', 'DIFFCARE', 'DIFFSENS', 'DIFFEYE', 'DIFFHEAR', 'VETSTAT', 'VETSTATD', 'VET01LTR', 'VET90X01', 'VET75X90', 'VET80X90', 'VET75X80', 'VETVIETN', 'VET55X64', 'VETKOREA', 'VET47X50', 'VETWWII', 'VETOTHER', 'VETYRS', 'PWSTATE2', 'PWTYPE', 'PWPUMAS', 'TRANWORK', 'CARPOOL', 'RIDERS', 'GCHOUSE', 'GCMONTHS', 'GCRESPON', 'PROBAI', 'PROBOTH']\n",
      "------------------\n",
      "76\n",
      "['HOMELAND', 'FARM', 'OWNERSHP', 'COMMUSE', 'ACREHOUS', 'TAXINCL', 'INSINCL', 'RENTMEAL', 'FOODSTMP', 'LINGISOL', 'KITCHEN', 'KITCHENORIG', 'FRIDGE', 'FRIDGEORIG', 'SINK', 'STOVE', 'PLUMBING', 'HOTWATER', 'SHOWER', 'TOILET', 'PHONEORIG', 'CILAPTOP', 'CIHAND', 'CIOTHCOMP', 'CIMODEM', 'CISAT', 'CIDSL', 'CIFIBER', 'CIBRDBND', 'CIDIAL', 'CIOTHSVC', 'SSMC', 'SEX', 'MARRINYR', 'DIVINYR', 'WIDINYR', 'RACAMIND', 'RACASIAN', 'RACBLK', 'RACPACIS', 'RACWHT', 'RACOTHER', 'SCHOOL', 'SCHLTYPE', 'EMPSTAT', 'LABFORCE', 'CLASSWKR', 'WRKLSTWK', 'ABSENT', 'LOOKING', 'WRKRECAL', 'DISABWRK', 'DIFFREM', 'DIFFPHYS', 'DIFFMOB', 'DIFFCARE', 'DIFFSENS', 'DIFFEYE', 'DIFFHEAR', 'VETSTAT', 'VET01LTR', 'VET90X01', 'VET75X90', 'VET80X90', 'VET75X80', 'VETVIETN', 'VET55X64', 'VETKOREA', 'VET47X50', 'VETWWII', 'VETOTHER', 'VETYRS', 'CARPOOL', 'GCHOUSE', 'GCRESPON', 'PROBOTH']\n"
     ]
    }
   ],
   "source": [
    "cols_enum100 = find_categorical(df, 100)\n",
    "cols_enum3 = find_categorical(df, 3)\n",
    "\n",
    "print len(cols_enum100)\n",
    "print cols_enum100\n",
    "\n",
    "print '------------------'\n",
    "\n",
    "print len(cols_enum3)\n",
    "print cols_enum3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training an ElasticNet model without any categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import cross_val_predict\n",
    "from sklearn.ensemble.forest import RandomForestRegressor\n",
    "from sklearn.grid_search import RandomizedSearchCV\n",
    "import pickle\n",
    "from scipy.stats import randint as sp_randint\n",
    "from operator import itemgetter\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(numCols)\n",
    "features.remove('INCEARN')\n",
    "target = 'INCEARN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5824645.8805276295"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[target].var()/500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Splitting the data by creating a hold-out set to asses performance of the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(dft[features], dft[target], test_size=0.25, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hkaren/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "alphas = np.logspace(-4, 7, 100)\n",
    "enet = linear_model.ElasticNet(l1_ratio=0.5)\n",
    "train_errors = list()\n",
    "test_errors = list()\n",
    "for alpha in alphas:\n",
    "    enet.set_params(alpha=alpha)\n",
    "    enet.fit(X_train, y_train)\n",
    "    train_errors.append(enet.score(X_train, y_train))\n",
    "    test_errors.append(enet.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.455239379418\n",
      "Optimal regularization parameter : 0.0001\n",
      "Model Score: 0.137928516996\n",
      "[  4.49807471e+02   3.52908615e+03   6.27953677e-01   2.06260187e+00\n",
      "   2.07700631e+03   3.55836088e+03   2.16124469e+04  -1.03427289e+01\n",
      "  -1.54824852e+00   4.29897492e+01  -1.14032864e+00   9.97495383e+00\n",
      "   2.70419274e+02  -2.61625566e+00   3.99181835e-03  -1.61946523e-02\n",
      "   6.17642350e+01  -8.03090890e-02   2.78363311e+02  -1.19164518e-02\n",
      "  -4.14169681e-02   1.69286456e+01  -1.68665347e-03   2.18681476e-02\n",
      "  -1.16662361e+00  -2.00416772e+00  -3.09908948e+03   3.29578364e+02\n",
      "  -3.51304868e+02  -2.08686166e+04   4.41358573e+03   4.40000217e+00\n",
      "  -9.70063683e+02  -4.22297556e+04  -1.41577093e+03   1.47902936e+02\n",
      "  -8.39143199e+02  -7.21475248e+02  -2.07676344e+02  -4.26342182e+02\n",
      "   8.84543409e+00   3.44488933e+00  -3.89659344e+03  -1.62150488e+03\n",
      "   2.73200086e+00   5.03779391e+01  -1.29996501e+00   5.95538896e+00\n",
      "  -8.76838852e-01  -7.76192649e+02   1.75653981e+01   1.03203611e+00\n",
      "   6.49885051e-01  -7.51654687e-02  -5.00057036e-04   7.44560105e-02\n",
      "   5.38452201e+03   3.95393130e-01   1.88379061e-02   2.85104384e+03\n",
      "  -3.09040724e+02  -1.18590095e+03   1.06089760e+03   3.47365699e+03\n",
      "   4.93413087e+03   1.64531453e+03   6.95336396e+02   1.95489470e+01\n",
      "   1.44498858e+03   4.30666137e+03  -3.36552575e+03  -4.59502808e+03\n",
      "   9.30124122e+01  -9.23415509e+01   1.19205918e+02  -4.86950079e+02\n",
      "  -1.50510941e+02   9.55281967e+01   1.13826374e+03  -9.44535080e+02\n",
      "  -5.42466187e+02   1.21228502e+03  -4.80967492e+01   5.44570281e+02\n",
      "   1.12630178e+03  -6.37829186e+02  -1.49009821e+03  -1.48758219e+02\n",
      "   1.11687798e+03  -1.98629882e+02  -1.87676067e+01   6.26148356e+02\n",
      "  -4.37970964e+03   8.69304161e+02  -1.43984171e+03  -4.71155124e+03\n",
      "  -4.96008604e+03   2.31850845e+04  -1.85804567e+03  -1.01067013e+03\n",
      "   2.37126719e-01   2.04211585e+00   2.53788012e+00   1.48021757e+00\n",
      "  -7.57396173e-01   2.59412185e+00  -8.47105454e-01   2.38600739e-02\n",
      "   8.92780962e-01  -3.81246061e-01  -5.76820821e-01   1.31974942e+00\n",
      "   1.84989971e+00   1.98054063e+00   2.54070225e+00  -2.68376513e-01\n",
      "   3.41623331e+00  -6.35523478e-01  -3.20595085e-01  -1.18481525e+00\n",
      "   1.37202949e+00   1.89611745e+00  -1.77530521e+00   1.98067536e+00\n",
      "  -1.67613269e+00   1.45552738e+00  -2.17890027e+00   2.35770142e+00\n",
      "  -8.04245191e-01   2.98951596e+00   2.16486807e-01   2.73742713e+00\n",
      "  -1.06172146e+00  -6.88730347e-02  -1.13946174e+00  -1.51653457e+00\n",
      "  -2.10748037e+00  -1.22147747e+00  -2.14035659e-01   1.26579238e+00\n",
      "  -6.06360643e-02   2.49430390e+00  -3.12188107e+00   2.58336110e-01\n",
      "  -2.66008541e+00  -8.85705010e-01  -2.32310141e+00  -2.69559076e+00\n",
      "  -2.50234186e+00  -1.58301060e+00   1.18748409e+00  -1.46747709e+00\n",
      "  -1.71564456e+00  -1.11490858e+00   1.83844711e+00   5.56573136e-01\n",
      "   1.35901816e+00   1.33656045e+00   2.20370030e+00  -2.01951776e+00\n",
      "  -4.66443532e-01  -3.24894731e-01   1.51967111e+00  -8.34835711e-01\n",
      "   2.62366670e+00  -9.48380082e-01   7.70617460e-01  -4.78478680e-01\n",
      "   1.09263551e-01  -1.23679988e+00  -6.86250699e-01   1.32973014e+00\n",
      "  -1.58245441e+00   2.20190553e+00  -1.84637226e+00   2.55455617e-01\n",
      "  -2.28148442e+00  -2.18259811e+00  -1.85904500e+00  -2.71231639e-01\n",
      "   5.01745282e+02   4.67678685e-02  -1.37617099e-04  -2.69202393e+03\n",
      "   3.22455512e+03  -3.99566538e+02   4.11941883e+03  -2.25765337e+02\n",
      "   1.45931296e+02   9.20724757e+02  -3.59041206e+02  -6.12621112e+02\n",
      "  -1.00508510e+03   6.45500974e+02   2.95707904e+02  -3.49696364e+03\n",
      "   2.25070335e+03  -1.20192031e+03  -1.45187643e+04   1.73181861e+03\n",
      "   6.73993632e+02  -8.54611895e+03   2.80451896e+03   3.77939691e+03\n",
      "  -1.48333278e+03   9.20096382e+00  -1.20027651e+04   1.22517495e+02\n",
      "  -4.02292616e+00   3.98989549e+01  -2.97391082e+02   2.16279377e+01\n",
      "  -2.10924733e+02  -2.43031688e+03   1.40025428e+00   1.09362926e+03\n",
      "  -7.67149008e+02   2.01567948e+03  -6.58909277e+02   8.63130485e+00\n",
      "  -1.62648888e+03   9.86481395e+00  -2.51132685e+00  -1.25297360e-02\n",
      "   4.17309170e-01   6.82389301e-04   8.43259936e-01  -1.06310243e-03\n",
      "   5.48745963e+02  -4.18124222e-02   9.15360842e-02   3.75938523e+01\n",
      "  -2.92058403e+02  -2.19269868e+01   8.35472408e-02  -8.56821531e+02\n",
      "  -4.22942905e-01   6.57015335e-05   3.36859739e+03  -3.02794773e+02\n",
      "  -6.93511453e+02  -1.54515971e+03  -1.61152407e+03   2.24048476e+02\n",
      "   5.04761153e+02   3.52024383e+02  -1.68354520e+03   6.13152995e+04\n",
      "  -1.33979906e+04   1.50227351e+03  -9.98364979e+03   5.69314138e+01\n",
      "  -1.25428782e+03   1.03010226e+02   1.13878367e-01   2.41168062e+01\n",
      "  -2.46832295e-01   1.49835965e+04  -1.73542432e+03   5.26629558e+02\n",
      "   5.31329556e+00   9.23123435e+00  -2.44107810e+01  -2.19625786e+00\n",
      "   1.69516474e+00  -4.02791986e+00   7.91625653e+00   8.46113413e+03\n",
      "  -9.66151217e+02  -6.92873238e+01   3.20486280e+03   6.55471329e+02\n",
      "  -1.38224538e+02  -8.15575839e+01   1.38593900e+02   6.67929338e+02\n",
      "   5.99708259e+02   8.79482482e+01   1.19886019e+03  -1.94772396e+02\n",
      "  -4.80118236e+01  -6.26342963e+01   2.88882735e+02  -3.43341276e+00\n",
      "   5.15155749e+02   4.40756749e+02   8.48094280e+01  -5.80576237e+02\n",
      "  -4.26936280e+02  -4.95282507e+02   4.78813706e+01  -9.19019061e-01\n",
      "  -2.40428792e-02   1.33869793e+02  -8.74465509e-02   5.65445619e-01\n",
      "  -1.47406308e-02   5.38819678e+02   3.83354370e+02  -1.10465079e+03\n",
      "   7.79283823e+02  -8.10562627e+02   1.11852928e+03  -4.03795888e+02\n",
      "  -1.83043101e+03   1.22824458e+03  -6.49644816e+02   1.34921853e+03\n",
      "  -5.23336963e+02  -1.62746982e+03   1.64348944e+03   2.68161276e+03\n",
      "  -7.29808633e+01  -7.04867077e+01   1.39114067e+02  -7.62105237e+03\n",
      "  -8.43319652e+03   8.28669078e+02  -7.31123177e+03   2.09189411e+04\n",
      "  -1.00467568e+03   1.84546444e+00   1.05705147e-01   3.57818214e-01\n",
      "  -4.02127117e+02   4.71078340e-02  -8.50861518e-01  -4.25198591e+01\n",
      "  -2.43919186e+03   1.23627973e+02   5.39923368e+01  -2.22449607e-01\n",
      "   4.04116369e-01   2.08795696e+03   1.73845049e+03  -3.82780535e+03\n",
      "   1.44741476e+01  -3.95508812e+01  -1.51440042e+01  -7.60849448e+01\n",
      "  -1.64990120e+01  -1.60334773e+00  -1.73701359e+00  -2.22982789e+00\n",
      "  -2.54343135e+00   6.99410682e-01  -2.29194650e+00   1.97835629e-02\n",
      "   6.18810701e-01  -8.70611274e-01  -2.53315908e-02   6.81632901e-01\n",
      "  -2.05573430e+00  -8.33529223e-01  -2.49446359e+00  -9.34112148e-01\n",
      "  -4.43065994e-01  -2.16401609e+00   3.82945703e-02   2.10653486e+00\n",
      "   1.22163758e+00  -1.03343887e+00  -1.11029450e+00   7.69198500e-01\n",
      "  -1.01180133e+00   1.25376452e+00  -1.17517951e+00   1.51502418e+00\n",
      "  -1.86993897e+00   2.04364871e+00  -3.32616185e+00  -7.48025178e-01\n",
      "  -2.81381697e+00   4.51269262e-01  -8.19749542e-01   1.27000776e+00\n",
      "   6.45461897e-01   2.02770847e+00   1.00250371e+00   2.07505448e-01\n",
      "  -2.26624558e+00  -1.10594654e+00  -6.92384238e-01   2.40468900e+00\n",
      "  -9.95908557e-01   1.82587258e+00   1.51395181e+00   1.03300505e+00\n",
      "   2.58497828e+00   2.29482106e+00   3.57209294e-01  -1.62226847e+00\n",
      "   4.92088164e-01   2.62458332e+00   8.27359301e-02  -2.17930437e-01\n",
      "  -1.22276938e+00  -4.30580265e-01  -1.34338448e+00  -2.00005334e+00\n",
      "   1.75289944e+00   7.26712629e-01   3.62535127e-02  -2.52510622e-01\n",
      "   4.17603822e-01  -1.79795286e+00   3.18993410e-01   7.58583982e-02\n",
      "  -9.70298055e-02  -1.14279031e-01   1.21492361e+00   5.61910283e-01\n",
      "  -1.36902082e+00   1.27634470e+00  -2.31096386e+00   2.20448418e+00\n",
      "  -1.02663703e+00   2.05854688e+00   2.23581091e+00   2.03126019e+00\n",
      "   4.57127602e-01  -5.27703276e+01  -7.54473498e+01]\n"
     ]
    }
   ],
   "source": [
    "i_alpha_optim = np.argmax(test_errors)\n",
    "print np.max(test_errors)\n",
    "alpha_optim = alphas[i_alpha_optim]\n",
    "print(\"Optimal regularization parameter : %s\" % alpha_optim)\n",
    "print (\"Model Score: %s\" % enet.score(X_test, y_test))\n",
    "\n",
    "enet.set_params(alpha=alpha_optim)\n",
    "coef_ = enet.fit(dft[features], dft[target]).coef_\n",
    "print coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2e94726f90>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbPd7XBv7dOXmLOD0I2jnE66/diC2BASZDYJgbXpttQcEmdX5tdwKoblMbmmpl1sBmaXp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\nIiIiIjIxDAKIiIiIiEzM/wATkEJqcmcnEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e10233610>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 384
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(alphas, train_errors, label='Train')\n",
    "plt.semilogx(alphas, test_errors, label='Test')\n",
    "plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',\n",
    "           linewidth=3, label='Optimum on test')\n",
    "plt.legend(loc='lower left')\n",
    "plt.ylim([0, 0.6])\n",
    "plt.xlabel('Regularization parameter')\n",
    "plt.ylabel('Performance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39800.608498413887"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, enet.predict(X_test))**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
